今年的世界人工智能大会（WAIC）可谓热闹非凡，据说有的展台甚至一度拥挤到工作人员都难以进入。 在出圈的众多机器人和终端产品之外，另一个领域也值得我们关注：科学智能（AI for Science，AI4S）。 机器之心报道 编辑：+0 在本届大会上，科学智能的战略地位被提到了新高度，作为十大核心方向之一，拥有专属论坛和多个交叉议题。这并非偶然，自从 AlphaFold 用惊人的效 率解决了困扰生物学界很长时间的难题， 科学智能 就已经证明，它不是未来的幻想，而是正在重塑科学根基的现实力量。 由复旦大学与上海科学智能研究院（上智院）联合主办的「星河启智·科学智能开放合作论坛」，为观察这一领域的变革趋势提供了一个窗口。 金力院士的呼吁描绘了「做什么」的宏大蓝图，而复旦大学特聘教授、上智院院长、无限光年创始人漆远则给出了「怎么做」的具体路径， 他在 星河启智 科学智能开放平台发布环节 的技术演讲中将 科学智能 的当前进展定义为 「科学智能 2.0 时代」：一个以领域科学家为中心，让 AI 进化为能理解科学家意 图、默契协作的「合作伙伴」的时代。 当强大的算力、前沿算法与具体的科学需求交织，未来将走向何方？从「超级科 ...

Core Insights - The WAIC2025 Star River Intelligent Open Cooperation Forum was held, focusing on building an open and collaborative scientific intelligence ecosystem [1] - Multiple innovative achievements were announced, including the Early Chinese Civilization Multimodal Large Model and the Global Academic Cooperation Initiative [1][3] Group 1: Early Chinese Civilization Multimodal Large Model - The Early Chinese Civilization Multimodal Large Model was officially released, developed by Fudan University, Shanghai Intelligent Research Institute, and Shanghai Chuangzhi Academy [3] - The model encompasses 100TB of specialized corpus, SFT data, and evaluation sets, pioneering cross-modal intelligent alignment of civilization spatiotemporal data [3] - It supports the Chinese Civilization AI Agent platform, enabling multi-step reasoning and complex task planning, benefiting education, research, and cultural industries [3] Group 2: Global Academic Cooperation Initiative - A global initiative was launched by top international scientists, including Nobel Prize winners, aiming to break the "data divide" and ensure AI benefits reach every corner of the globe [3] - The initiative outlines four core objectives: building open-source scientific infrastructure, initiating multinational interdisciplinary scientific programs, cultivating international scientific talent, and establishing a fair value-sharing mechanism [3][4] Group 3: Star River Intelligent Open Platform - The Star River Intelligent Open Platform was launched, designed to accelerate scientific discovery and provide comprehensive infrastructure for scientists and AI engineers [6] - It aims to enhance cross-disciplinary collaboration and address key scientific challenges, significantly speeding up scientific discoveries [6] - The platform includes the "Guanxin" large model, which formalizes complex clinical diagnosis processes into a multi-agent collaborative system for cardiovascular specialties [6] Group 4: Ethical Review AI Agent "Yijian" - The ethical review AI agent "Yijian" was introduced, capable of automatic rule review and risk labeling, enhancing review efficiency and compliance [7] - It has been trialed at Fudan University and its affiliated Zhongshan Hospital, ensuring data security and supporting the generation of review reports [7]

Core Viewpoint - The competition among top-tier hospitals in China has intensified in the AI sector, with a significant focus on developing medical AI models to enhance healthcare services and operational efficiency [1][3]. Group 1: AI Model Development - As of mid-2023, approximately 300 medical AI models have been developed in China, with nearly half released in the first half of the year [3]. - Major hospitals like Shanghai Zhongshan, Ruijin, Renji, and Xinhua have launched AI models targeting various medical fields, including cardiology and pediatrics [1][3]. - The RuiPath pathology model, developed by Ruijin Hospital in collaboration with Huawei, has been recognized internationally for its capabilities in AI-assisted pathology diagnosis [4]. Group 2: AI Applications in Healthcare - AI applications in hospitals are expanding, with digital guides and AI models being utilized for patient consultations and decision-making support [3][4]. - The "CardioMind" model from Fudan University Zhongshan Hospital aims to enhance cardiology diagnostics and treatment, leveraging extensive patient data [5]. - AI models are expected to handle up to 80% of routine tasks, allowing doctors to focus on complex cases and patient interactions [7]. Group 3: Challenges and Ethical Considerations - The rapid advancement of AI technology poses challenges, including the need for robust data governance and ethical standards in medical AI applications [8][9]. - Concerns regarding the accuracy and reliability of general AI models in specialized medical fields have been raised, highlighting the importance of using validated technologies [8]. - Ensuring patient data security and privacy is critical, with measures such as data anonymization and psychological support being implemented in AI model development [8].

Core Insights - The healthcare industry is rapidly adopting AI models to create an "autonomous driving" system for medical practices, with top-tier hospitals competing in AI capabilities [1][6] - In the first half of this year, approximately 300 medical AI models have been developed in China, with nearly half released in this timeframe, indicating a significant trend towards AI integration in healthcare [3] - AI applications in hospitals are expanding beyond simple tasks, with digital guides and AI models being utilized for various medical specialties, enhancing efficiency and patient care [3][4] Group 1: AI Model Development - Major hospitals like Zhongshan, Ruijin, Renji, and Xinhua have launched AI models for various diseases, including cardiology and pediatrics, showcasing the competitive landscape [1][3] - The RuiPath pathology model, developed by Ruijin Hospital in collaboration with Huawei, has been recognized internationally for its capabilities in AI-assisted pathology diagnosis [3][4] - The "CardioMind" model from Zhongshan Hospital represents a significant advancement in cardiology, aiming to provide expert-level diagnostic support to physicians [4][5] Group 2: AI Applications and Impact - AI models are being integrated into clinical workflows, with applications in clinical decision support, pre-consultation, medical record generation, and imaging diagnostics, accounting for 53% of usage scenarios [3] - The establishment of Tsinghua AI Agent Hospital illustrates the potential for fully automated healthcare environments, where AI can handle diagnostic tasks with high accuracy [6] - The use of AI in hospitals is expected to allow physicians to focus more on complex cases, as AI can manage up to 80% of routine tasks [6] Group 3: Challenges and Considerations - The rapid advancement of AI technology poses challenges in data management and ethical considerations, particularly regarding patient privacy and data security [7][8] - Hospitals face difficulties in accessing and utilizing high-quality data for training AI models, as much of this data is contained within closed systems [7][8] - The need for regulatory frameworks to keep pace with technological advancements in AI healthcare applications is becoming increasingly critical [7]